MP-MPPI Motion Primitive Guided Sampling-Based Optimizer for Model Predictive Control

Jul 16, 2026

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This paper proposes MP-MPPI, an extension of the Model Predictive Path Integral (MPPI) method that incorporates motion primitives to add structured sampling inside a real-time sampling-based optimization loop. Evaluating motion primitives and perturbed control sequences improves convergence toward globally optimal solutions while addressing sampling-based path-planning limitations. Implementation on a quadcopter simulator and testing on obstacle-field navigation show enhanced exploration of the control space without sacrificing the fast, reactive behavior required for online MPC.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

Introduction

Model Predictive Control and MPC Optimization

MPPI and Exploration–Exploitation Challenges

Related Work and Limitations

7 Jul 2026 MP-MPPI: A Motion Primitive Guided Sampling-Based Optimizer for Model Predictive Control Marlon G. Mathisen ∗ , Aksel Vaaler ∗ , Olav Egeland ∗ , Eleni Kelasidi ∗ ∗ Norwegian University of Science and Technology, Dept. of Mechanical and Industrial Engineering, Norway Abstract: This paper proposes a novel method that extends the Model Predictive Path Integral (MPPI) method with motion primitives for additional structured sampling, which enhances the convergence towards a globally optimal solution. By evaluating motion primitives and perturbed control sequences in a real-time sampling-based optimization loop, this work addresses the limitations of the path planning capabilities of sampling-based controllers. The algorithm is implemented on a quadcopter simulator and tested on an obstacle field navigation task. It is demonstrated that the proposed approach enhances exploration of the control space while maintaining the fast, reactive behavior required for real-time control. Keywords: Model predictive control, Numerical methods for optimal control, Real-time optimal control, Non-smooth and discontinuous optimal control 1. INTRODUCTION The demand for robust, agile controllers has grown significantly in recent years, as researchers pivot to devel- arXiv :2607 .06123v1 obstacle fields in this setting is particularly demanding, making it well-suited to evaluate novel control strategies (Hanover et al., 2024) . While professional quadcopter pilots train extensively to handle these challenging environments (Pfeiffer and Scaramuzza, 2021), classic path planning approaches often fail to handle the fast-moving, changing environments, and frequently fail to operate in real-time (Karur et al., 2021) . Therefore, the development of controllers that bridge the gap between high-level planning and low-level control is essential for matching human performance in real-world environments. Model Predictive Control (MPC) bridges this gap by leveraging a system model to predict future states and optimize control inputs (Garc´ıa et al., 1989) . The MPC problem is typically solved using gradient-based optimization to satisfy system constraints while minimizing a defined cost function. In recent years, the growing capabilities of parallel computing have enabled the implementation of MPC on highperformance hardware, such as GPUs, allowing for faster and more scalable real-time control in complex robotic systems (Abughalieh and Alawneh, 2019) . Model Predictive Path Integral (MPPI) is one such implementation that uses a sampling-based technique for optimization (Williams et al., 2017) . MPPI works by sam- ⋆ Corresponding author: [marlon.g.mathisen@ntnu.no](marlon.g.mathisen@ntnu.no) Fig. 1. Overview of the proposed MP-MPPI algorithm. The motion primitives are generated from the cost function and dynamics model, and are used to inform the model predictive path integral controller with additional samples. pling small perturbations on an initial control input and calculating the cost relating to some control objective for each perturbation. These perturbed control inputs are then fused with a higher weight given to lower cost perturbations, producing a new optimal control sequence. A strength with sampling-based methods is that they do not require differentiable system dynamics, allowing greater freedom to tailor the cost function to the problem at hand. The magnitude of the perturbations added to the initial control sequence naturally defines a maximum change in the control sequence per iteration. This limits how quickly the control input can be transformed into a new optimal control sequence when the environment changes, which can make the controller unresponsive and slow to adapt to new changes. While proven to be a powerful optimizer, this approach introduces challenges in balancing exploration and exploitation. Increasing the magnitude of the perturbations to increase global optimality often induces instability in

FAQs

How does MP-MPPI extend MPPI for model predictive control?

MP-MPPI extends MPPI by adding motion primitives, which provide structured additional sampling guided by cost and dynamics within the real-time optimization loop.

What problem does the paper target in sampling-based controllers?

It targets limited path-planning capabilities caused by the exploration–exploitation trade-off, where increasing perturbations can improve global optimality but may lead to instability and poor real-time adaptation.

How is the proposed approach validated?

The method is implemented on a quadcopter simulator and tested on an obstacle-field navigation task to demonstrate improved exploration while retaining fast reactive control behavior.

Jul 16, 2026

Upload by Rowan